clmm
covariate model when there are no baseline covariates in the modelnewdata
for predict()
that does not contain the outcome variableadd_samples()
: calculation of end()
of mcmc samples fixedadd_samples()
when used in parallel with thinning fixedpredict()
can now handle newdata
with missing outcome values; predicted values for cases with missing covariates are NA
(prediction with incomplete covariates is planned to be implemented in the future)get_MIdat()
and plot_imp_distr()
when only one variable has missing valuesncores
has changed to n.cores
for consistency with n.iter
, n.chains
, etc.coxph_imp()
does no longer use a counting process implementation but uses the likelihood in JAGS directly via the zeros trickpredict()
now has an argument length
to change number of evaluation pointssummary()
, predict()
, traceplot()
, densplot()
, GR_crit()
, MC_error()
now have an argument exclude_chains
that allows to specify chains that should be omittedcitation()
now refers to a manuscript on arXivglmm_lognorm
available to impute level-1 covariates with a log-normal mixed modelresiduals()
and plot()
available for (some of the) main analysis types (details see documentation)models
added to get_models()
so that the user can specify to also include models for complete covariates (which are then positioned in the sequence of models according to the systematic used in JointAI). Specification of a model not needed for imputation prints a notification.JointAI
objects (most types) now also include residuals and fitted values (so far, only using fixed effects)Xl
is no longer included in data_list
when it is not used in the modelsubset
when specified as vectorsummary
: range of iterations is printed correctly now when argument end
is usedsummary()
calls GR_crit()
with argument autoburnin = FALSE
unless specified otherwise via ...
inits
is specified as a function, the function is evaluated and the resulting list passed to JAGS (previously the function was passed to JAGS)simong
and simWide
have changed (more variables, less subjects)imp_pars = TRUE
(when user specified via monitor_params
or subset
)survreg_imp
the sign of the regression coefficient is now opposite to match the one from survreg
add_samples()
: bug that copied the last chain to all other chains fixedXc
, so that specification of functions of covariates in auxiliary variables works betterdensplot()
issue (all plots showed all lines) fixedplot_all()
, densplot()
, and traceplot()
limit the number of plots on one page to 64 when rows and columns of the layout are not user specified (to avoid the ‘figure margins too large’ error)longDF
example data: new version containing complete and incomplete categorical longitudinal variables (and variable names L1 and L2 changed to c1 and c2)list_impmodels()
changed to list_models()
(but list_impmodels()
is kept as an alias for now)clm_imp()
and clmm_imp()
: new functions for analysis of ordinal (mixed) models
coxph_imp()
: new function to fit Cox proportional hazards models with incomplete (baseline) covariatesno_model
allows to specify names of completely observed variables for which no model should be specified (e.g., “time” in a mixed model)ridge = TRUE
allows to use shrinkage priors on the precision of the regression coefficients in the analysis modelplot_all()
can now handle variables from classes Date
and POSIXt
parallel
allows different MCMC chains to be sampled in parallelncores
allows to specify the maximum number of cores to be usedseed
added for reproducible results; also a sampler (.RNG.name
) and seed value for the sampler (.RNG.seed
) are set or added to user-provided initial values (necessary for parallel sampling and reproducibility of results)plot_imp_distr()
: new function to plot distribution of observed and imputed valuesRinvD
is no longer selected to be monitored in random intercept model (RinvD
is not used in such a model)summary()
: reduced default number of digitsmeth
now uses default values if only specified for subset of incomplete variablesget_MIdat()
: argument minspace
added to ensure spacing of iterations selected as imputationsdensplot()
: accepts additional options, e.g., lwd
, col
, …list_models()
replaces the function list_impmodels()
(which is now an alias)coef()
method added for JointAI
object and summary.JointAI
objectconfint()
method added for JointAI
objectprint()
method added for JointAI
objectsurvreg_imp()
added to perform analysis of parametric (Weibull) survival modelsglme_imp()
added to perform generalized linear mixed modelingtraceplot()
, densplot()
: specification of nrow
AND ncol
possible; fixed bug when only nrow
specifiedlme_imp()
: fixed error in JAGS model when interaction between random slope variable and longitudinal variableplot_all()
uses correct level-2 %NA in titlesimWide
: case with no observed bmi values removedtraceplot()
, densplot()
: ncol
and nrow
now work with use_ggplot = TRUE
traceplot()
, densplot()
: error in specification of nrow
fixeddensplot()
: use of color fixedsubset
now return random effects covariance matrix correctlysummary()
displays output with rowname when only one node is returned and fixed display of D
matrixGR_crit()
: Literature reference correctedpredict()
: prediction with varying factor fixedplot_all()
uses xpd = TRUE
when printing text for character variableslist_impmodels()
uses linebreak when output of predictor variables exceeds getOption("width")
summary()
now displays tail-probabilities for off-diagonal elements of D
predict()
: now also returns newdata
extended with predictionmonitor_params
is now checked to avoid problems when only part of the main parameters is selectedmd.pattern()
now uses ggplot, which scales better than the previous versionlm_imp()
, glm_imp()
and lme_imp()
now ask about overwriting a model fileanalysis_main = T
stays selected when other parameters are followed as wellget_MIdat()
: argument include
added to select if original data are included and id variable .id
is added to the datasetsubset
argument uses same logit as monitor_params
argumentlm_imp()
, glm_imp()
and lme_imp()
now take argument trunc
in order to truncate the distribution of incomplete variablessummary()
now omits auxiliary variables from the outputimp_par_list
is now returned from JointAI modelscat_vars
is no longer returned from lm_imp()
, glm_imp()
and lme_imp()
, because it is contained in Mlist$refs
plot_all()
function addeddensplot()
and traceplot()
optional with ggplotdensplot()
option to combine chains before plottingNHANES
, simLong
and simWide
addedlist_impmodels
to print information on the imputation models and hyperparametersparameters()
added to display the parameters to be/that were monitoredset_refcat()
added to guide specification of reference categoriesmd_pattern()
: does not generate duplicate plot any moreget_MIdat()
: imputed values are now filled in in the correct orderget_MIdat()
: variables imputed with lognorm
are now included when extracting an imputed datasetget_MIdat()
: imputed values of transformed variables are now included in imputed datasetsmeth
argument